If you're paranoid, or even slightly guarded, about posting photos of yourself online, what you're about to read might incite you to make a scorched-earth dive for the delete key.

According to a group of researchers from Carnegie Mellon University (CMU), led by associate professor of Information Technology and Public Policy, Alessandro Acquisti, it is now even easier to identify and access your personal information from photos posted on the Internet.

Using cloud computing, facial recognition and public information found on social media profiles like Facebook and online dating sites, Acquisti and his team conducted experiments that yielded some revealing results.

In one experiment, Acquisti and his team uniquely identified 4,900 out of 5,800 anonymous dating site members.They did this by using a search engine and an application programming interface (API) that culled about 275,000 publicly available profile photos from Facebook in a particular city. Then, they did the same with publicly accessible images from a dating site in same city. Using Pittsburgh Pattern Recognition (PittPatt), a facial recognition program developed at CMU, Acquisti easily found matches between the dating site and Facebook profile images.

According to a 2005 CMU survey, around 90 percent of Facebook users use their real name on their profiles, so by matching profiles, Acquisti was able to correctly identify individuals.

Another experiment involved Acquisti's team taking webcam photos of almost 100 students in attempt to match the image with each student's Facebook profile. As students filled out surveys, their webcam photos were ran through PittPatt. Around 31 percent of the students were identified in under four seconds.

In arguably the most disturbing experiment, Acquisti used students who had their date of birth and hometown publicly posted on their social network profile to predict their Social Security numbers (SSNs). By using techniques from a previous study showing that SSNs can be somewhat accurately guessed using public information, Acquisti correctly identified the first five digits of SSNs in 16 percent of the students. After four attempts, the accuracy rate jumped to 27 percent.

Acquisti presented his study on August 5 at the Black Hat secuirity conference in Las Vegas. He told Computer World his findings present "ominous implications for privacy" and that he was at the conference "to raise awareness of what I feel is going to happen."